Paper Citation Record · LEDGER

Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2311.09921.

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pith.paper-citation-record.v1
2311.09921 v2

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:31:48.004329Z

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A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-24T01:35:55.497080Z

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Outbound references

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Pith citing papers

Observation b5e0af43-1ae9-4a50-8071-3cdf61b49aca · inbound

Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA cites this paper.

Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 211

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Observation bee8e2fe-fd53-4224-bd09-510bed066f92 · inbound

PhenomXPNR: An improved gravitational wave model linking precessing inspirals and NR-calibrated merger-ringdown cites this paper.

PhenomXPNR: An improved gravitational wave model linking precessing inspirals and NR-calibrated merger-ringdown Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 73

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Observation b067ff2b-1a09-475b-bfc4-f3ac3568739e · inbound

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference cites this paper.

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 51

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Observation 71012c28-d960-4b8c-8b76-f9ecef764116 · inbound

Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset cites this paper.

Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 58

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Observation 2390d74d-5854-482f-9d88-024866e4e5b5 · inbound

Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset cites this paper.

Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 58

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Observation 48419be3-ea86-4205-8061-a327691a62ec · inbound

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data cites this paper.

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 112

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Observation b2c39745-97f4-4f55-9e42-9a73ec6e20a3 · inbound

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data cites this paper.

Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 112

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Observation 9090b3e7-e26d-4973-a16a-b6f344366c6c · inbound

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit cites this paper.

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:32:43.297533Z

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Observation 04ce1d73-d0a2-44b2-835f-e4dd3229b8d3 · inbound

The first decade of gravitational-wave measurements of black hole spins cites this paper.

The first decade of gravitational-wave measurements of black hole spins Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 208

Resolution: unresolved; no resolver link, observed 2026-08-02T12:20:52.321401Z

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Observation e5f73f3f-3964-49e3-b5e2-7daf16bae568 · inbound

Joint inference for gravitational-wave signal and noise glitch: Method and application cites this paper.

Joint inference for gravitational-wave signal and noise glitch: Method and application Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-13T05:20:32.887480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.